## DATA ANALYST COURSE FEES IN SINGAPORE

### Live Virtual

Instructor Led Live Online

##### S 2,950
###### S 1,588

• IABAC® & JAINx® Certification
• 6-Month | 200+ Learning Hours
• 20 HOURS LEARNING A WEEK
• 10 Capstone & 1 Client Project
• 365 Days Flexi Pass + Cloud Lab
• Internship + Job Assistance

## Enquire Now

### Blended Learning

Self Learning + Live Mentoring

##### S 1,470
###### S 906

• Self Learning + Live Mentoring
• IABAC® & JAINx® Certification
• 10 Capstone & 1 Client Project
• Job Assistance
• 24*7 Learner assistance and support

## Enquire Now

### Corporate Training

• Instructor-Led & Self-Paced training
• Customized Learning Options
• Industry Expert Trainers
• Case Study Approach
• 24*7 Cloud Lab

Enquire Now

## BEST DATA ANALYTICS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

## SYLLABUS OF DATA ANALYST CERTIFICATION IN SINGAPORE

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deploying
• Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS

• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data

MODULE 2 : HARNESSING DATA

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's  Minimum Sample Size
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Biased Random Sampling Methods
• Sampling Error
• Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean, Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution
• Z Value / Standard Value
• Empherical Rule  and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING

• Hypothesis Testing Introduction
• P- Value, Confidence Interval
• Parametric Hypothesis Testing Methods
• Hypothesis Testing Errors : Type I And Type Ii
• One Sample T-test
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

• Correlation Introduction
• Direct/Positive Correlation
• Indirect/Negative Correlation
• Regression
• Choosing Right Method

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

MODULE 1: DATA ANALYTICS FOUNDATION

• Visual Perspective
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.

MODULE 4: DECISION MODELING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool

MODULE 6: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML

• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

## ABOUT DATAMITES DATA ANALYST TRAINING IN SINGAPORE

According to Global Data, the size of the data and analytics market reached \$93.1 billion in 2021 and is projected to grow at a CAGR of over 8% from 2021 to 2026.The demand for data analytics in Singapore is on the rise due to the government's focus on building a Smart Nation and the increasing adoption of digital technologies by businesses across various sectors.

To meet the increasing demand for data analysts in Singapore, DataMites is offering a specialized six-month Certified Data Analyst Course in Singapore that equips participants with the foundational skills needed to succeed in the field. The comprehensive curriculum covers essential topics such as data science basics, visual analytics, and predictive modeling, with hands-on experience provided through real-world projects and internships. The certification is approved by IABAC, ensuring the quality of education.

According to a study by IMD Business School, Singapore is ranked first in the world for digital competitiveness, which indicates a high demand for digital skills such as data analytics. Obtaining a data analytics course in Singapore can provide a significant boost to your career prospects. With the city-state's rapidly growing tech industry, there is an increasing demand for data analysts, and possessing a recognized certification can give you a competitive edge.

The DataMites Certified Data Analyst Training in Singapore offers a valuable opportunity for individuals seeking to capitalize on the high demand for skilled data analysts in the city. By gaining proficiency in data analytics, participants can become sought-after professionals in this rapidly growing industry. Enroll in the program now to take advantage of these opportunities and start your data analytics career.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Singapore.

## ABOUT DATA ANALYST COURSE IN SINGAPORE

Data analytics is the process of examining and interpreting large sets of data in order to uncover useful insights, patterns, and trends that can inform decision-making.

Yes, anyone can pursue a career in data analytics with the right skills, knowledge, and training. However, it is important to have a strong foundation in mathematics, statistics, and computer programming to succeed in this field.

Essential competencies for data analytics include proficiency in statistics, data manipulation and analysis, programming languages such as Python or R, critical thinking, problem-solving, data visualization, and effective communication.

Data analytics provides businesses with insights into their operations and customers, which can be used to make data-driven decisions and improve overall performance.

Data analytics relies on a variety of tools and techniques to manipulate and analyze large sets of data. Some commonly used tools include SQL, Python, R, Tableau, Power BI, and Excel. Techniques used in data analytics include data mining, data visualization, predictive modeling, and machine learning.

In Singapore, the training fee for Data Analytics courses varies based on the program and institution, with a range of 646.24 SGD to 1454.04 SGD.

When it comes to learning data analytics in Singapore, DataMites is considered one of the best institutes in the city. With a comprehensive curriculum that covers all the key topics and hands-on training provided by experienced faculty members, DataMites' data analytics courses in Singapore are designed to provide students with the skills and knowledge they need to succeed in this exciting field. The institute also offers flexible schedules and a range of certification programs to suit different career goals and skill levels.

The field of data analytics has seen tremendous growth in recent years, and as a result, there is a high demand for skilled data analysts across various industries. With the increasing amount of data available, companies require professionals who can effectively analyze and interpret data to drive business decisions. A career in data analytics can offer a wide range of job opportunities, including roles such as data analyst, data scientist, business analyst, and more. Individuals with a strong foundation in data analytics and the ability to apply analytical techniques to real-world problems can expect to have a bright future in this field.

The DataMites Certified Data Analyst Course is an excellent choice for those interested in learning data analytics. The program covers all the fundamental concepts and skills required for a successful career in data analytics, including programming languages, statistical analysis, data visualization, and machine learning. The course is designed by industry experts and provides hands-on experience with real-world datasets, making it a highly valuable choice for those aspiring to become data analysts.

According to glassdoor.com, the average salary for a data analyst in Singapore is 13,550 SGD a month.

## FAQ’S OF DATA ANALYST COURSE IN SINGAPORE

DataMites offers several advantages in their data analytics course, including comprehensive coverage of key data analytics concepts and skills, hands-on experience with real-world projects, personalized mentoring and guidance from industry experts, and globally recognized certifications. Additionally, the program is designed to provide flexibility, with online and offline learning options, as well as multiple payment plans to suit individual needs. Overall, DataMites' data analytics course provides a valuable opportunity for individuals to acquire the necesSGDy skills and expertise to excel in the field of data analytics.

DataMites' certified data analyst training in Singapore stands out for several reasons. Firstly, the program is designed by industry experts and covers all essential concepts and skills required for a career in data analytics, including programming languages, statistical analysis, data visualization, and machine learning. Secondly, the course provides hands-on experience with real-world datasets, making it a valuable choice for aspiring data analysts. Additionally, the certification is approved by IABAC, a global organization that ensures the quality of education. Finally, the program offers a Flexi-Pass option, allowing students to learn at their own pace and convenience.

The DataMites' comprehensive data analytics training program spans over six months and includes 20 hours of weekly instruction.

Even though DataMites® offers in-person training, it is currently only available in India. However, we also offer online Certified Data Analytics Courses in Singapore that are equally impactful and immersive.

DataMites offers a Certified Data Analyst Course suitable for individuals who want to pursue a career in data science or data analytics without prior coding knowledge. The course offers a complete introduction to the subject, making it an ideal choice for beginners. Enroll now in the Data Analytics Training in Singapore by DataMites if you're interested in learning analytics.

The cost of DataMites' certified data analytics training may depend on the mode of training chosen. In Singapore, the usual range for a certified data analytics course is from 452.37 SGD to 1130.92 SGD.

DataMites offers its students the convenience of making payments through multiple channels. Accepted modes of payment include cash, debit cards, checks, and credit cards like Visa, Mastercard, American Express, as well as PayPal and net banking.

Upon successful completion of the DataMites Certified Data Analyst Training, students will be awarded an IABAC® certification, which is globally recognized and validates their expertise and knowledge in data analytics. IABAC is a renowned professional organization that offers internationally recognized certification programs for data analysts, business analysts, and data scientists.

If you opt to take the online exam at exam.iabac.org, you will receive the results instantly. The issuance of e-certificates typically takes 7-10 business days as per the guidelines provided by IABAC.

Flexi-Pass is an exceptional feature offered by DataMites that enables students to attend their classes at their own pace. The feature provides access to live and recorded sessions of the enrolled course, which remains valid for a specified duration from the date of enrollment. This is a great advantage for individuals with busy schedules or job commitments who cannot attend regular classes.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

• 1. Job connect
• 2. Resume Building
• 3. Mock interview with industry experts
• 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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